class_docstr: Add 3 to every category.
code: def transform(self, X):
      X = self._convert_and_check_X(X)
      # Increment everything by three to account for the fact that
      # np.NaN will get an index of two, and coalesced values will get index of
      # one, index of zero is not assigned to also work with sparse data
      X_data = X.data if sparse.issparse(X) else X
      X_data += 3
      return X
  
file_path: auto-sklearn/autosklearn/pipeline/implementations/CategoryShift.py
imports: import numpy
  from scipy import sparse
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.utils import check_array
label_desc: Use image node_id 4 for calling the CategoryShift obj's underlying member method code with example usage: obj.transform(X) and returns: X